– By Ritesh Mohan Srivastava
Generative Artificial Intelligence has emerged as a buzzword today. A lot of organizations are now investing into understanding the benefits of Generative AI and how it can enhance productivity and aid the growth of their business. There are also a number of conversations on how GenAI could impact jobs of millions. So, before we jump into the challenges and opportunities of GenAI, let’s begin with understanding what GenAI really implies.
Buzz Around Generative AI: What does it mean?
GenAI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on. The applications for this technology are growing with each passing day – ranging from writing high-quality software code in a much lesser time, discovering new molecules, to training trustworthy conversational chatbots grounded on enterprise data. GenAI is now even being used to create synthetic data to build more robust and trustworthy AI models that can generate statistically probable outputs when prompted.
Generative AI is revolutionizing the ways of working of financial institutions and Fintech companies. While rapid advancements in GenAI have opened up new possibilities across various industries, it is the fintech and banking sector that is poised to benefit immensely from this innovative technology. Generative AI can play a crucial role in fraud detection and risk assessment and can significantly enhance customer experience as well as offer personalized financial planning.
Apart from enhancing customer satisfaction, improving decision-making, and reducing risks through better monitoring of fraud and risk, GenAI could add between US$ 200 billion and US$ 340 billion in value across the banking, wholesale, and retail sectors through greater productivity, as per a recent report by McKinsey.
In the past, the banking and financial services sectors have benefited significantly from previously existing artificial intelligence applications, in areas such as customer operations and personalized marketing. In today’s era, Generative AI applications could deliver additional benefits and help fintechs and banks deliver an overall enhanced experience.
Opportunities for using Generative AI
Fraud Detection and Prevention: Generative AI models can continuously monitor and analyze incoming data streams for potential fraud. This allows for immediate detection and taking remedial action on a timely basis. With real-time detection of fraudulent incidents operational disruptions, legal consequences, and reputational damages can be reduced apart from financial losses to organizations.
Risk Assessment and Credit Scoring: A credit scoring model allows lenders to distinguish between good & bad loans and give an estimate of the probability of default. With the help of Generative AI, credit scoring models can be built by evaluating various factors such as credit history, income, employment records, and customer behavior. Financial institutions can derive deeper insights into the creditworthiness of borrowers with these models helping them make more informed lending decisions and mitigate potential risks
Natural Language Processing (NLP) for Customer Service: Natural language processing (NLP) is a branch of Generative AI that focuses on helping computers understand the way humans write and speak. The use of Natural Language Processing (NLP) techniques for improving risk management in fintech is advancing at a rapid pace. NLP gives machines the ability to read and understand human languages and it can be used to analyze customer behavior and identify patterns that may point to fraudulent activity. Sentiment analysis is part of the Natural Language Processing (NLP) techniques that consists of extracting emotions related to some raw texts. Here, machines break down unstructured data, for example, social media posts, going through a phase of data pre-processing to create structured data that can be used for analysis.
Personalized Financial Planning: Generative AI can analyze vast amounts of financial data and patterns including spending habits, income, and investment preferences, to generate tailored financial plans, budgeting techniques, and savings options. It can analyze expenses, financial goals, and spending habits to provide tailored recommendations and strategies to achieve financial objectives.
Challenges in using Generative AI
Generative AI has immense potential but it comes with its own set of challenges. Its widespread use could increase data and privacy risks as Generative AI uses large amounts of data to further generate models that are susceptible to bias, low quality, unauthorized access, and potential loss. Privacy is one of the most significant concerns particularly when models are not trained with privacy-preserving algorithms, as they become vulnerable to privacy risks and attacks. The chances of inadvertently generating content that violates an individual’s privacy prevails as AI models learn from training data – enormous databases obtained from multiple sources.
Similarly, there are legal and ethical considerations with GenAI. The risk of exposing an individual’s identity through produced data is always there which makes it difficult to comply with laws governing the use of AI. Striking a balance between technological advancement and compliance is the moot question while implementing GenAI.
AI-generated material can easily travel across borders, creating disputes between various legal systems, intellectual property rules, and jurisdictional challenges. It could throw challenges in determining ownership and rights for AI-generated content which can cause a conflict of interest.
Additionally, there are deep apprehensions about the limited traceability and irreproducibility of GenAI outcomes. Lastly, lack of strategic roadmap (including investment priorities) and governance are major challenges around Generative AI.
Conclusion
To conclude, GenAI has a host of benefits that can aid in the growth of the businesses. However, in order to make Generative AI a success an organization needs to invest in its people. To overcome the challenges thrown by Generative AI, it is essential to invest in ethical AI training and emphasize the testing of models.
For the successful enactment of GenAI in the organization implementation of an effective AI governance strategy is vital with all the stakeholders viz. data scientists and engineers; data providers, user experience designers, functional leaders, and product managers coming together to assess the risks the technology might pose enterprise-wide. In other words, a risk management framework that acts as a playbook for risk executives will help in managing new risks as well as a slew of business, legal, and regulatory challenges.
There will be an increasing buzz for GenAI in the times to come, as more companies join in and find new use cases as technology becomes an integral part of their everyday processes.
(Ritesh Mohan Srivastava is the Chief Data Scientist at BharatPe.)
(Disclaimer: Views expressed are personal and do not reflect the official position or policy of Financial Express Online. Reproducing this content without permission is prohibited.)